By Topic

An adaptive learning-like solution of random early detection for congestion avoidance in computer networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Misra, S. ; Sch. of Inf. Technol., Indian Inst. of Technol., Kharagpur ; Oommen, B.J. ; Yanamandra, S. ; Obaidat, M.S.

In this paper, we present an adaptive learning (specifically, learning automata) Like (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as learning automata like random early detection (LALRED), is founded on the principles of operations of the existing random early detection (RED) congestion avoidance mechanisms, augmented with a LAL philosophy. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED, which was chosen as the benchmark for performance comparison purposes.

Published in:

Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on

Date of Conference:

10-13 May 2009